Chapter 6 Community composition
6.1 Taxonomy overview
6.1.1 Stacked barplot
genome_counts_filt_met<-genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
filter(count > 0) #filter 0 counts
genome_counts_filt_met$Elevation<-as.factor(genome_counts_filt_met$Elevation)
# Create an interaction variable for elevation and sample
genome_counts_filt_met$interaction_var <- interaction(genome_counts_filt_met$sample, genome_counts_filt_met$Elevation)
# Plot stacked barplot
ggplot(genome_counts_filt_met, aes(x=interaction_var,y=count,fill=phylum, group=phylum))+ #grouping enables keeping the same sorting of taxonomic units
geom_bar(stat="identity", colour="white", linewidth=0.1)+ #plot stacked bars with white borders
scale_fill_manual(values=phylum_colors) +
labs(y = "Relative abundance", x="Elevation (m)") +
guides(fill = guide_legend(ncol = 3)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
legend.position = "top",
legend.title = element_blank(),
legend.text = element_text(size=7))+
scale_x_discrete(labels = function(x) gsub(".*\\.", "", x)) +
facet_wrap(~Transect, scales = "free", labeller = as_labeller(function(label) gsub(".*\\.", "", label))) #only show elevation label6.1.1.2 Bacteria phyla in Aisa transect
phylum_summary_aisa <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
group_by(sample,phylum) %>%
summarise(relabun=sum(count)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
filter(Transect=="Aisa")%>%
filter(relabun > 0)
# Number of bacterial phyla Aisa
length(unique(phylum_summary_aisa$phylum))[1] 13
# Bacteria phyla
phylum_summary_aisa %>%
group_by(phylum) %>%
summarise(total_mean=mean(relabun*100, na.rm=TRUE),
total_sd=sd(relabun*100, na.rm=TRUE)) %>%
mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>%
arrange(-total_mean) %>%
dplyr::select(phylum,total) %>%
tt()| phylum | total |
|---|---|
| p__Bacillota_A | 42.18±15.99 |
| p__Bacteroidota | 39.09±17.57 |
| p__Pseudomonadota | 5.27±6.75 |
| p__Bacillota | 3.99±3.67 |
| p__Verrucomicrobiota | 3.53±3.26 |
| p__Campylobacterota | 3.29±3.44 |
| p__Desulfobacterota | 2.48±1.93 |
| p__Fusobacteriota | 1.35±1.33 |
| p__Bacillota_C | 1.29±1.53 |
| p__Cyanobacteriota | 0.82±0.49 |
| p__Chlamydiota | 0.7±0.63 |
| p__Bacillota_B | 0.53±0.41 |
| p__Actinomycetota | 0.18±0.09 |
6.1.1.3 Bacteria phyla in Aran transect
phylum_summary_aran <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
group_by(sample,phylum) %>%
summarise(relabun=sum(count)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
filter(Transect=="Aran")%>%
filter(relabun > 0)
# Number of bacterial phyla Aran
length(unique(phylum_summary_aran$phylum))[1] 14
# Bacteria phyla
phylum_summary_aran %>%
group_by(phylum) %>%
summarise(total_mean=mean(relabun*100, na.rm=TRUE),
total_sd=sd(relabun*100, na.rm=TRUE)) %>%
mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>%
arrange(-total_mean) %>%
dplyr::select(phylum,total) %>%
tt()| phylum | total |
|---|---|
| p__Bacillota_A | 41.7±16.74 |
| p__Bacteroidota | 36.85±14.71 |
| p__Pseudomonadota | 7.27±11.02 |
| p__Campylobacterota | 5.5±8.27 |
| p__Bacillota | 4.64±5.26 |
| p__Fusobacteriota | 4.01±8.68 |
| p__Desulfobacterota | 2.75±2.22 |
| p__Verrucomicrobiota | 2.38±3.15 |
| p__Spirochaetota | 1.22±1.19 |
| p__Bacillota_C | 1.14±1.18 |
| p__Cyanobacteriota | 0.72±0.54 |
| p__Deferribacterota | NA |
| p__Bacillota_B | 0.44±0.45 |
| p__Actinomycetota | 0.3±0.26 |
6.1.1.4 Bacteria phyla in Sentein transect
phylum_summary_sentein <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
group_by(sample,phylum) %>%
summarise(relabun=sum(count)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
filter(Transect=="Sentein")%>%
filter(relabun > 0.)
# Number of bacterial phyla Sentein
length(unique(phylum_summary_sentein$phylum))[1] 15
# Bacteria phyla
phylum_summary_sentein %>%
group_by(phylum) %>%
summarise(total_mean=mean(relabun*100, na.rm=TRUE),
total_sd=sd(relabun*100, na.rm=TRUE)) %>%
mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>%
arrange(-total_mean) %>%
dplyr::select(phylum,total) %>%
tt()| phylum | total |
|---|---|
| p__Bacteroidota | 40.72±14.71 |
| p__Bacillota_A | 34.82±14.88 |
| p__Bacillota | 6.17±10.94 |
| p__Pseudomonadota | 5.19±5.3 |
| p__Campylobacterota | 4.8±5.42 |
| p__Desulfobacterota | 4.77±8.22 |
| p__Spirochaetota | 4.76±5.8 |
| p__Chlamydiota | 3.19±3.26 |
| p__Fusobacteriota | 2.17±5.62 |
| p__Verrucomicrobiota | 1.34±1.49 |
| p__Cyanobacteriota | 0.78±0.37 |
| p__Actinomycetota | 0.77±1.29 |
| p__Bacillota_C | 0.61±0.51 |
| p__Bacillota_B | 0.16±0.07 |
| p__Deferribacterota | NA |
6.1.1.5 Bacteria phyla in Tourmalet transect
phylum_summary_tourmalet <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
group_by(sample,phylum) %>%
summarise(relabun=sum(count)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
filter(Transect=="Tourmalet")
# Number of bacterial phyla Tourmalet
length(unique(phylum_summary_tourmalet$phylum))[1] 16
# Bacteria phyla
phylum_summary_tourmalet %>%
group_by(phylum) %>%
summarise(total_mean=mean(relabun*100, na.rm=TRUE),
total_sd=sd(relabun*100, na.rm=TRUE)) %>%
mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>%
arrange(-total_mean) %>%
dplyr::select(phylum,total) %>%
tt()| phylum | total |
|---|---|
| p__Bacillota_A | 41.87±15.42 |
| p__Bacteroidota | 39.29±15.9 |
| p__Bacillota | 5.87±5.94 |
| p__Pseudomonadota | 5.3±5.15 |
| p__Campylobacterota | 2.32±2.71 |
| p__Desulfobacterota | 1.58±2.02 |
| p__Verrucomicrobiota | 0.94±1.13 |
| p__Bacillota_C | 0.83±0.53 |
| p__Cyanobacteriota | 0.53±0.82 |
| p__Fusobacteriota | 0.4±0.81 |
| p__Actinomycetota | 0.39±0.73 |
| p__Spirochaetota | 0.23±0.51 |
| p__Deferribacterota | 0.16±0.51 |
| p__Bacillota_B | 0.16±0.23 |
| p__Chlamydiota | 0.11±0.53 |
| p__Synergistota | 0.03±0.16 |
6.1.2 Phylum relative abundances
phylum_summary <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
group_by(sample,phylum) %>%
summarise(relabun=sum(count))
phylum_summary %>%
group_by(phylum) %>%
summarise(total_mean=mean(relabun*100, na.rm=T),
total_sd=sd(relabun*100, na.rm=T)) %>%
mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>%
arrange(-total_mean) %>%
dplyr::select(phylum,total) %>%
tt()| phylum | total |
|---|---|
| p__Bacillota_A | 40.6±15.93 |
| p__Bacteroidota | 38.3±15.88 |
| p__Pseudomonadota | 5.83±7.87 |
| p__Bacillota | 4.59±6.37 |
| p__Campylobacterota | 3.47±5.63 |
| p__Desulfobacterota | 2.68±4.05 |
| p__Verrucomicrobiota | 1.53±2.43 |
| p__Bacillota_C | 0.95±1.05 |
| p__Fusobacteriota | 0.91±3.7 |
| p__Cyanobacteriota | 0.4±0.6 |
| p__Spirochaetota | 0.22±0.97 |
| p__Bacillota_B | 0.19±0.32 |
| p__Actinomycetota | 0.17±0.52 |
| p__Chlamydiota | 0.1±0.61 |
| p__Deferribacterota | 0.05±0.27 |
| p__Synergistota | 0.01±0.08 |
phylum_arrange <- phylum_summary %>%
group_by(phylum) %>%
summarise(mean=mean(relabun)) %>%
arrange(-mean) %>%
select(phylum) %>%
pull()
phylum_summary %>%
filter(phylum %in% phylum_arrange) %>%
mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
geom_jitter(alpha=0.5) +
theme_minimal()6.2 Taxonomy boxplot
6.2.1 Family
family_summary <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
group_by(sample,family) %>%
summarise(relabun=sum(count))
family_summary %>%
group_by(family) %>%
summarise(mean=mean(relabun),sd=sd(relabun)) %>%
arrange(-mean) %>%
tt()| family | mean | sd |
|---|---|---|
| f__Lachnospiraceae | 2.798160e-01 | 0.1323151296 |
| f__Bacteroidaceae | 2.132576e-01 | 0.1045524501 |
| f__Tannerellaceae | 1.122881e-01 | 0.0756060991 |
| f__ | 6.556348e-02 | 0.0820773598 |
| f__Helicobacteraceae | 3.474580e-02 | 0.0562678198 |
| f__Marinifilaceae | 3.441228e-02 | 0.0255200299 |
| f__UBA3700 | 2.777694e-02 | 0.0330290932 |
| f__Desulfovibrionaceae | 2.680077e-02 | 0.0404627923 |
| f__Ruminococcaceae | 2.435712e-02 | 0.0226880677 |
| f__Rikenellaceae | 1.900710e-02 | 0.0176572117 |
| f__Erysipelotrichaceae | 1.767814e-02 | 0.0269603016 |
| f__Oscillospiraceae | 1.696763e-02 | 0.0140960977 |
| f__Coprobacillaceae | 1.273032e-02 | 0.0336927310 |
| f__Mycoplasmoidaceae | 1.210259e-02 | 0.0270262454 |
| f__Enterobacteriaceae | 1.077111e-02 | 0.0651262303 |
| f__Fusobacteriaceae | 9.053249e-03 | 0.0370456782 |
| f__Akkermansiaceae | 8.677367e-03 | 0.0108836065 |
| f__CAG-239 | 7.003283e-03 | 0.0098019806 |
| f__LL51 | 6.656864e-03 | 0.0216352391 |
| f__Anaerotignaceae | 6.551732e-03 | 0.0074061779 |
| f__UBA3830 | 5.185462e-03 | 0.0171469663 |
| f__Gastranaerophilaceae | 4.003188e-03 | 0.0059975426 |
| f__Muribaculaceae | 3.991492e-03 | 0.0409006227 |
| f__Butyricicoccaceae | 3.919222e-03 | 0.0050520985 |
| f__CAG-274 | 3.051617e-03 | 0.0060828463 |
| f__Acutalibacteraceae | 2.723615e-03 | 0.0047471119 |
| f__Anaerovoracaceae | 2.683446e-03 | 0.0040557355 |
| f__Pumilibacteraceae | 2.544525e-03 | 0.0041701010 |
| f__UBA1997 | 2.383785e-03 | 0.0080270055 |
| f__CAG-508 | 2.324078e-03 | 0.0063109845 |
| f__Brevinemataceae | 2.191263e-03 | 0.0097068603 |
| f__Peptococcaceae | 1.937490e-03 | 0.0031961496 |
| f__Rhodocyclaceae | 1.936615e-03 | 0.0194735479 |
| f__DTU072 | 1.792884e-03 | 0.0055918788 |
| f__UBA660 | 1.742458e-03 | 0.0054664441 |
| f__MGBC116941 | 1.714356e-03 | 0.0090980913 |
| f__Massilibacillaceae | 1.502892e-03 | 0.0024528334 |
| f__Eggerthellaceae | 1.377525e-03 | 0.0037174906 |
| f__Enterococcaceae | 8.454990e-04 | 0.0070848098 |
| f__CALTSX01 | 5.198677e-04 | 0.0053270584 |
| f__Chlamydiaceae | 5.170637e-04 | 0.0030492819 |
| f__Mucispirillaceae | 4.593259e-04 | 0.0026630822 |
| f__CALVMC01 | 4.496143e-04 | 0.0018583730 |
| f__Clostridiaceae | 4.212744e-04 | 0.0029132915 |
| f__Acidaminococcaceae | 4.004438e-04 | 0.0015777220 |
| f__UBA1242 | 3.719350e-04 | 0.0014704441 |
| f__RUG11792 | 3.717143e-04 | 0.0019425248 |
| f__CAG-465 | 3.497207e-04 | 0.0015695529 |
| f__Microbacteriaceae | 3.199134e-04 | 0.0026999464 |
| f__CAG-288 | 2.985206e-04 | 0.0017514912 |
| f__Streptococcaceae | 2.479473e-04 | 0.0018438849 |
| f__Anaplasmataceae | 2.435113e-04 | 0.0021508694 |
| f__Hepatoplasmataceae | 2.362221e-04 | 0.0024205565 |
| f__Aeromonadaceae | 2.327082e-04 | 0.0022486997 |
| f__Peptostreptococcaceae | 1.412306e-04 | 0.0014471826 |
| f__Rhodobacteraceae | 1.371859e-04 | 0.0014057373 |
| f__Xanthomonadaceae | 9.245878e-05 | 0.0009474206 |
| f__Synergistaceae | 7.840493e-05 | 0.0008034115 |
| f__Lactobacillaceae | 4.203166e-05 | 0.0004306963 |
| f__Turicibacteraceae | 0.000000e+00 | 0.0000000000 |
family_arrange <- family_summary %>%
group_by(family) %>%
summarise(mean=sum(relabun)) %>%
arrange(-mean) %>%
select(family) %>%
pull()
family_summary %>%
left_join(genome_metadata %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
left_join(sample_metadata,by=join_by(sample==EHI_number)) %>%
filter(family %in% family_arrange[1:20]) %>%
mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
filter(relabun > 0) %>%
ggplot(aes(x=relabun, y=family, group=family, color=phylum)) +
scale_color_manual(values=phylum_colors[-8]) +
geom_jitter(alpha=0.5) +
theme_minimal() +
labs(y="Family", x="Relative abundance", color="Phylum")6.2.2 Genus
genus_summary <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
group_by(sample,genus) %>%
summarise(relabun=sum(count)) %>%
filter(genus != "g__")
genus_summary %>%
group_by(genus) %>%
summarise(mean=mean(relabun),sd=sd(relabun)) %>%
arrange(-mean) %>%
tt()| genus | mean | sd |
|---|---|---|
| g__Bacteroides | 1.725925e-01 | 0.0994470682 |
| g__Parabacteroides | 1.103381e-01 | 0.0767310570 |
| g__Roseburia | 5.077999e-02 | 0.0743624266 |
| g__Phocaeicola | 3.874581e-02 | 0.0435882803 |
| g__JAAYNV01 | 3.526845e-02 | 0.0735025649 |
| g__Odoribacter | 3.376557e-02 | 0.0254845145 |
| g__Helicobacter_J | 2.983823e-02 | 0.0400574428 |
| g__CAG-95 | 1.659217e-02 | 0.0240471187 |
| g__Alistipes | 1.555908e-02 | 0.0156740595 |
| g__Kineothrix | 1.544351e-02 | 0.0584815409 |
| g__MGBC136627 | 1.451743e-02 | 0.0230914933 |
| g__Mycoplasmoides | 1.139564e-02 | 0.0269054026 |
| g__Hungatella_A | 1.113992e-02 | 0.0670243947 |
| g__Anaerotruncus | 1.006956e-02 | 0.0112599653 |
| g__Velocimicrobium | 9.687390e-03 | 0.0159382742 |
| g__Enterocloster | 9.506772e-03 | 0.0098451213 |
| g__Fusobacterium_A | 9.053249e-03 | 0.0370456782 |
| g__Acetatifactor | 8.989245e-03 | 0.0139417426 |
| g__Akkermansia | 8.677367e-03 | 0.0108836065 |
| g__Clostridium_Q | 7.770136e-03 | 0.0138182274 |
| g__Bilophila | 7.347777e-03 | 0.0088824113 |
| g__Lawsonia | 7.130440e-03 | 0.0356135290 |
| g__Intestinimonas | 6.565049e-03 | 0.0061282758 |
| g__Lacrimispora | 6.397950e-03 | 0.0078443852 |
| g__Lachnotalea | 5.679000e-03 | 0.0086820363 |
| g__Desulfovibrio | 5.604601e-03 | 0.0085133819 |
| g__MGBC140009 | 5.533083e-03 | 0.0134005650 |
| g__Extibacter | 5.301402e-03 | 0.0438086011 |
| g__Coprobacillus | 5.255707e-03 | 0.0160178991 |
| g__Ventrimonas | 5.038137e-03 | 0.0083320432 |
| g__NHYM01 | 4.907573e-03 | 0.0427691471 |
| g__Dielma | 4.863951e-03 | 0.0065480876 |
| g__Eisenbergiella | 4.698317e-03 | 0.0069699563 |
| g__CHH4-2 | 4.463440e-03 | 0.0044701587 |
| g__RGIG4733 | 4.252340e-03 | 0.0103379868 |
| g__Negativibacillus | 4.038946e-03 | 0.0054742770 |
| g__Thomasclavelia | 3.858196e-03 | 0.0117540652 |
| g__Hungatella | 3.440712e-03 | 0.0046402483 |
| g__C-19 | 3.334625e-03 | 0.0097460323 |
| g__Citrobacter | 3.199667e-03 | 0.0207814167 |
| g__UMGS1251 | 2.883830e-03 | 0.0066485409 |
| g__Oscillibacter | 2.697945e-03 | 0.0038453712 |
| g__CAZU01 | 2.687821e-03 | 0.0062923086 |
| g__Breznakia | 2.569391e-03 | 0.0076690515 |
| g__Copromonas | 2.559264e-03 | 0.0037548588 |
| g__Mailhella | 2.486840e-03 | 0.0034349591 |
| g__Pseudoflavonifractor | 2.349829e-03 | 0.0028250595 |
| g__Intestinibacillus | 2.290036e-03 | 0.0027696754 |
| g__Escherichia | 2.232632e-03 | 0.0160096591 |
| g__Brevinema | 2.191263e-03 | 0.0097068603 |
| g__MGBC165282 | 2.146060e-03 | 0.0051869977 |
| g__Rikenella | 2.123615e-03 | 0.0033608364 |
| g__Morganella | 2.019952e-03 | 0.0206983454 |
| g__Robinsoniella | 2.000942e-03 | 0.0198167695 |
| g__Parabacteroides_B | 1.950084e-03 | 0.0064473560 |
| g__Hafnia | 1.939718e-03 | 0.0112340944 |
| g__Fluviibacter | 1.936615e-03 | 0.0194735479 |
| g__JAIHAL01 | 1.909337e-03 | 0.0043937983 |
| g__CAJLXD01 | 1.809579e-03 | 0.0041385093 |
| g__Marseille-P3106 | 1.729807e-03 | 0.0025326958 |
| g__UBA866 | 1.535703e-03 | 0.0026149730 |
| g__MGBC116941 | 1.433278e-03 | 0.0090996222 |
| g__Duncaniella | 1.426611e-03 | 0.0146184139 |
| g__Stoquefichus | 1.418761e-03 | 0.0046100976 |
| g__Limenecus | 1.393591e-03 | 0.0029887106 |
| g__JAAYQI01 | 1.271605e-03 | 0.0020016472 |
| g__RGIG6463 | 1.268390e-03 | 0.0028982477 |
| g__Lawsonibacter | 1.177407e-03 | 0.0016547788 |
| g__Scatousia | 1.174334e-03 | 0.0033329854 |
| g__MGBC101980 | 1.147280e-03 | 0.0043856608 |
| g__Hespellia | 1.102861e-03 | 0.0080033094 |
| g__Clostridium_AQ | 1.075206e-03 | 0.0036972777 |
| g__Tidjanibacter | 1.062223e-03 | 0.0029392145 |
| g__Fournierella | 1.021882e-03 | 0.0022586979 |
| g__Eggerthella | 9.946416e-04 | 0.0031801193 |
| g__OM05-12 | 9.566920e-04 | 0.0022952664 |
| g__CALXRO01 | 9.555497e-04 | 0.0060396607 |
| g__CALURL01 | 9.088777e-04 | 0.0021653921 |
| g__Harryflintia | 8.906013e-04 | 0.0020436518 |
| g__MGBC133411 | 8.872215e-04 | 0.0021982622 |
| g__Scatacola_A | 8.685187e-04 | 0.0028129788 |
| g__JALFVM01 | 8.392923e-04 | 0.0020111922 |
| g__Bacteroides_G | 8.358400e-04 | 0.0024999742 |
| g__Ventrisoma | 8.334289e-04 | 0.0015664780 |
| g__CAG-269 | 8.185801e-04 | 0.0029513181 |
| g__IOR16 | 8.132853e-04 | 0.0024147185 |
| g__CAG-873 | 7.794020e-04 | 0.0079864937 |
| g__Buttiauxella | 7.562352e-04 | 0.0062186311 |
| g__14-2 | 7.242943e-04 | 0.0014229865 |
| g__Ureaplasma | 7.069544e-04 | 0.0022811697 |
| g__Scatocola | 6.903112e-04 | 0.0024144468 |
| g__Dysosmobacter | 6.880800e-04 | 0.0012817741 |
| g__Muricomes | 6.843281e-04 | 0.0023723812 |
| g__Anaerovorax | 6.779999e-04 | 0.0017521412 |
| g__UBA7185 | 6.690992e-04 | 0.0018509746 |
| g__Butyricimonas | 6.467068e-04 | 0.0016133034 |
| g__MGBC131033 | 6.443020e-04 | 0.0015998136 |
| g__Evtepia | 6.309865e-04 | 0.0010453519 |
| g__CAJMNU01 | 6.281598e-04 | 0.0008908563 |
| g__Beduini | 5.914594e-04 | 0.0013148807 |
| g__Muribaculum | 5.550053e-04 | 0.0056871120 |
| g__Scandinavium | 5.392506e-04 | 0.0034848792 |
| g__Lactonifactor | 5.340135e-04 | 0.0013169750 |
| g__CALTSX01 | 5.198677e-04 | 0.0053270584 |
| g__CAG-485 | 5.128011e-04 | 0.0052546481 |
| g__Merdicola | 5.109908e-04 | 0.0018183944 |
| g__Ventrenecus | 4.954074e-04 | 0.0024443063 |
| g__UMGS1202 | 4.885966e-04 | 0.0016971062 |
| g__Copranaerobaculum | 4.827706e-04 | 0.0029167211 |
| g__NSJ-61 | 4.574747e-04 | 0.0014044413 |
| g__Faecimonas | 4.467204e-04 | 0.0017621834 |
| g__RGIG8482 | 4.415175e-04 | 0.0020991286 |
| g__Faecivivens | 4.313538e-04 | 0.0008927244 |
| g__RGIG9287 | 4.228193e-04 | 0.0021002237 |
| g__Sarcina | 4.212744e-04 | 0.0029132915 |
| g__Blautia_A | 4.144742e-04 | 0.0010034450 |
| g__Scatenecus | 4.103021e-04 | 0.0026534948 |
| g__Phascolarctobacterium | 4.004438e-04 | 0.0015777220 |
| g__Raoultibacter | 3.828831e-04 | 0.0011609168 |
| g__Caccovivens | 3.719350e-04 | 0.0014704441 |
| g__CAJTFG01 | 3.690163e-04 | 0.0010223015 |
| g__HGM11386 | 3.642876e-04 | 0.0016060882 |
| g__CAG-465 | 3.497207e-04 | 0.0015695529 |
| g__Amedibacillus | 3.457091e-04 | 0.0023834921 |
| g__Enterococcus_A | 3.276668e-04 | 0.0023252026 |
| g__UMGS2016 | 3.212305e-04 | 0.0012912770 |
| g__Emergencia | 3.205926e-04 | 0.0009702487 |
| g__Holdemania | 3.098282e-04 | 0.0010287045 |
| g__Blautia | 3.095400e-04 | 0.0011077317 |
| g__Protoclostridium | 3.067429e-04 | 0.0010837140 |
| g__Fimivivens | 3.043536e-04 | 0.0008104254 |
| g__RGIG7389 | 2.985465e-04 | 0.0005848732 |
| g__CAG-345 | 2.985206e-04 | 0.0017514912 |
| g__UBA7173 | 2.963722e-04 | 0.0030369115 |
| g__Bariatricus | 2.928468e-04 | 0.0008379528 |
| g__Agathobaculum | 2.787287e-04 | 0.0016389968 |
| g__CALXDZ01 | 2.660973e-04 | 0.0006132220 |
| g__UBA940 | 2.621830e-04 | 0.0009075589 |
| g__Microbacterium | 2.563077e-04 | 0.0026263723 |
| g__Aminipila | 2.506050e-04 | 0.0007479203 |
| g__Lactococcus | 2.479473e-04 | 0.0018438849 |
| g__Wolbachia | 2.435113e-04 | 0.0021508694 |
| g__Paramuribaculum | 2.432436e-04 | 0.0024925056 |
| g__Hepatoplasma | 2.362221e-04 | 0.0024205565 |
| g__Aeromonas | 2.327082e-04 | 0.0022486997 |
| g__WRHT01 | 2.186196e-04 | 0.0006955370 |
| g__Zhenpiania | 2.116559e-04 | 0.0012708329 |
| g__UBA5026 | 2.112884e-04 | 0.0009778887 |
| g__UMGS1663 | 1.999591e-04 | 0.0007286034 |
| g__MGBC107952 | 1.752651e-04 | 0.0009887232 |
| g__CALXEL01 | 1.725652e-04 | 0.0013728689 |
| g__CAG-273 | 1.482564e-04 | 0.0007864691 |
| g__Clostridioides | 1.412306e-04 | 0.0014471826 |
| g__Paracoccus | 1.371859e-04 | 0.0014057373 |
| g__JAAWBF01 | 1.286984e-04 | 0.0006144643 |
| g__JAFLTL01 | 1.270703e-04 | 0.0013020827 |
| g__Bacteroides_H | 1.267213e-04 | 0.0012985068 |
| g__RUG12867 | 9.254620e-05 | 0.0006129332 |
| g__Stenotrophomonas | 9.245878e-05 | 0.0009474206 |
| g__Rahnella | 8.365618e-05 | 0.0008059384 |
| g__Lumbricidophila | 6.360575e-05 | 0.0006517650 |
| g__UBA3263 | 5.098641e-05 | 0.0005224553 |
| g__Fructobacillus | 4.203166e-05 | 0.0004306963 |
| g__Clostridium | 0.000000e+00 | 0.0000000000 |
| g__Turicibacter | 0.000000e+00 | 0.0000000000 |
genus_arrange <- genus_summary %>%
group_by(genus) %>%
summarise(mean=sum(relabun)) %>%
filter(genus != "g__")%>%
arrange(-mean) %>%
select(genus) %>%
mutate(genus= sub("^g__", "", genus)) %>%
pull()
genus_summary %>%
left_join(genome_metadata %>% select(genus,phylum) %>% unique(),by=join_by(genus==genus)) %>%
left_join(sample_metadata,by=join_by(sample==EHI_number)) %>%
mutate(genus= sub("^g__", "", genus)) %>%
filter(genus %in% genus_arrange[1:20]) %>%
mutate(genus=factor(genus,levels=rev(genus_arrange[1:20]))) %>%
filter(relabun > 0) %>%
ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
scale_color_manual(values=phylum_colors) +
geom_jitter(alpha=0.5) +
theme_minimal() +
labs(y="Genus", x="Relative abundance", color="Phylum")